RESEARCH ARTICLES
Response of a natural phytoplankton
community from the Qingdao coast
(Yellow Sea, China) to variable CO2 levels
over a short-term incubation experiment
Haimanti Biswas1,*, Jin Jie2, Ying Li3, Guosen Zhang3, Zhuo-Yi Zhu3,
Ying Wu3, Guo-Ling Zhang2, Yan-Wei Li2, Su Mei Liu2 and Jing Zhang3
1
CSIR-National Institute of Oceanography, Regional Centre, 176 Lawson’s Bay Colony, Visakhapatnam 530 017, India
Key Laboratory of Marine Chemistry Theory and Technology Ministry of Education, Ocean University of China, 238 Songling Road,
Qingdao, 266100, P.R. China
3
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, 3663 Zhongshan Road North Shanghai 200062, China
2
Since marine phytoplankton play a vital role in stabilizing earth’s climate by removing significant amount
of atmospheric CO 2, their responses to increasing CO2
levels are indeed vital to address. The responses of a
natural phytoplankton community from the Qingdao
coast (NW Yellow Sea, China) was studied under
different CO 2 levels in microcosms. HPLC pigment
analysis revealed the presence of diatoms as a dominant
microalgal group; however, members of chlorophytes,
prasinophytes, cryptophytes and cyanophytes were also
present.  13CPOM values indicated that the phytoplankton community probably utilized bicarbonate ions as
dissolved inorganic carbon source through a carbon
concentration mechanism (CCM) under low CO2 levels,
and diffusive CO2 uptake increased upon the increase
of external CO2 levels. Although, considerable increase
in phytoplankton biomass was noticed in all CO2
treatments, CO 2-induced effects were absent. Higher
net nitrogen uptake under low CO2 levels could be
related to the synthesis of CCM components. Flow cytometry analysis showed slight reduction in the abundance of Synechococcus and pico-eukaryotes under the
high CO 2 treatments. Diatoms did not show any negative impact in response to increasing CO2 levels;
however, chlorophytes revealed a reverse tend. Heterotrophic bacterial count enhanced with increasing
CO2 levels and indicated higher abundance of labile
organic carbon. Thus, the present study indicates that
any change in dissolved CO2 concentrations in this
area may affect phytoplankton physiology and community structure and needs further long-term study.
causing significant changes in carbonate chemistry (increasing hydrogen ion concentrations, dissolved CO2 and
bicarbonate ions, and decreasing carbonate ion concentrations and pH; a process known as ‘ocean acidification’)2,3.
Increasing CO2 levels in seawater may potentially affect
marine phytoplankton communities4–6. Noticeable impacts of increasing CO2 levels on phytoplankton primary
production7,8 and community composition9–11 have been
clearly documented in several experiments and the responses were highly diverse. Presumably, the observed
differential behaviour of diverse groups of marine phytoplankton in response to increasing CO2 levels is principally
because of their dissimilar carbon metabolism patterns12.
Hence, in general, how marine phytoplankton would
respond to ongoing increasing CO2 levels needs to be
addressed and requires extensive area-specific studies.
Coastal waters can show high level of variability in
CO2 partial pressure and pH, and hence coastal phytoplankton communities can be less vulnerable to ocean
acidification. Virtually, the responses of phytoplankton
communities from Qingdao coastal waters to increasing
CO2 levels have not been studied so far. The present
short-term study discusses the effects of different CO2
levels on the natural phytoplankton assemblage from the
coastal waters of Qingdao (NW Yellow Sea, China). Although, the short-term incubation experiment may not
show any long-term changes in the study area, it can
show the responses of the plankton communities over a
short-term natural change.
Keywords: Diatoms, increasing CO2 levels, light stress
phytoplankton community, phytoplankton pigment, Qingdao coast.
Materials and methods
AS a consequence of huge amount of CO2 release, atmospheric CO2 concentration is increasing in an alarming
rate1 and also dissolving into the surface ocean
*For correspondence. (e-mail: [email protected])
CURRENT SCIENCE, VOL. 108, NO. 10, 25 MAY 2015
Study site, water sample collection and experimental
settings
Qingdao coast is a part of Yellow Sea (NW) and one of
the important marginal seas located between mainland
China and the Korean peninsula (Figure 1). This area
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derives its name from the colour of the silt-laden water
discharged into it by major Chinese rivers, including the
Yellow River, which flows into the Bohai, and the Yangtze. Surface sea water was collected (through a 200 m
mesh to remove large mesozooplankton) with the help of
a mechanized boat from the Qingdao coast in three 20 l
acid-cleaned carboys and brought back to the laboratory
for further experiments. The carboys were kept at 20C
under natural sunlight (10 : 14 h day–night period) for one
complete day to let the plankton community to acclimatize.
pCO2 was calculated using dissolved inorganic carbon
(DIC) and total alkalinity (TA) following the methodology of Lewis and Wallace13. All carbon chemistry parameters (pCO2, DIC, TA, pH) were measured in order to
calculate the initial pCO2 in the ambient waters, which
was  460 atm. Keeping this CO2 level as control (in
triplicate), two additional higher pCO2 levels ( 640 and
730 atm) were set in triplicate by adding sodium
bicarbonate followed by diluted HCl14. The final pH values of three different pCO2 levels were 7.99  0.008,
7.88  0.008 and 7.83  0.001 respectively.
After adjusting the pCO2 levels in 5 l transparent
polyethylene containers without any headspace, the bottles were tightly closed with a sealed lock and incubated
in triplicate in the same place where the carboys were
kept initially in similar conditions for the next 5 days under natural day–night period. Additional care was taken
to avoid in/out gassing by wrapping the sealed lock with
a parafilm strip. Sampling was done on the sixth day of
incubation. Each bottle was mixed gently everyday at a
particular time (at 10 am) in order to avoid biomass settlement at the bottom.
Sample analysis
DIC was analysed using the Apollo SciTech’s AC-C2
DIC analyzer fitted with a Li-Cor 6262 CO2 analyzer and
Certified Standard Reference Materials (CRMs by Dickson et al.15 ). Total alkalinity was measured according to
Dickson et al.15 using a 794 Basic Titrino (Metrohm).
Major nutrients (dissolved inorganic nitrogen (DIN), dissolved inorganic phosphatre (DIP) and silicate (Si))
were analysed colorimetrically with the help of an autoanalyzer (San + Analyzer; Skalar Analytical, The Netherlands) following the standard protocol of sea-water
analysis16,17.
For phytoplankton pigment analysis, 1 litre of sample
water was filtered using GF/F (47 mm) filters and were cut
into pieces, added with methanol, ground and phytoplankton pigment was extracted by an ultrasonicator
(VCX644, Sonics and Materials, USA) in an ice-bath.
Samples were then centrifuged (3000 rpm) and filtered
through clean 0.45 m PTFE membrane. They were analysed using the HPLC system, Agilent 1100 series
following the methodology of Zapata and Garrido18, and
Zapata et al.19. Pigments were quantified based on the
comparison of retention time and spectra with authentic
standards (chlorophyll a (chl a) and  -carotene were purchased from Sigma-Aldrich Company, USA. The rest 18
standards were purchased from DHI Company).
For particulate organic carbon (POC) and particulate
organic nitrogen (PON), 500 ml water sample was filtered
using a pre-combusted GF/F 47 mm filter and the filter
was dried at 40C overnight (inside a hot-air oven) and
exposed to HCl fume to avoid interference of particulate
inorganic carbon (PIC) if any. POC and PON were measured using a mass spectrometer (model: Delta plus XP)
connected with a Flash Elemental Analyzer. Urea and potassium nitrate (IAEA) were used as the working standard
and black carbon as the reference. Carbon and nitrogen
stable isotope ratios ( 13C and  15N) of particulate organic
matter (POM) was measured with the help of an isotopic
ratio mass spectrometer (model: Delta plus XP) using
PDB as a standard and calculated using the following
formula
 13CPOM = [{(13C/12C)sample/(13C/12C)standard }-1]  1000,
and
 15NPOM = [{(15N/14N)sample/(15N/14N)standard} – 1]  1000.
Figure 1. Map showing sampling station in the Qingdao coast,
Yellow Sea, China.
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Particulate organic phosphate (POP) was estimated using
the method described Aspila et al.20. For dissolved organic
carbon (DOC) analysis, 40 ml of sea water was filtered
through a 0.45 m filter fitted with a clean syringe and
collected in acid-clean brown glass bottles and kept frozen at –20C till further analysis. Later DOC was determined by high temperature catalytic oxidation with a
Shimadzu 5000(A) TOC analyzer. The uncertainty of the
concentration range is 2% for the instruments. The instrument blank (5–10 M) was subtracted from all measured
values. Autotrophic pico-plankton, Synechococcus and
heterotrophic bacteria were analysed with a three-colour
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Figure 2. Initial day and final day values of (a) total Chlorphyll a (Chl a); (b) particulate organic
carbon (POC); (c) particulate organic nitrogen (PON); (d) particulate organic phosphate (POP), and the
ratios (e) of Chlorphyll a to POC; ( f ) POC to PON; (g) POC to POP and (h) PON to POP under different
CO 2 levels (Average  SD, n = 3; the grey line in each figure separates the initial and final day values of
each parameter.)
FACScan TM bench flow cytometer (Becton Dickinson,
San Jose, CA USA) equipped with an air-cooled argon ion
laser providing 15 mW at 488 nm. Triplicate measurements
were made for each sample with precision higher than
7.2% (relative standard deviation). Paired t-test was conducted to show the level of significance, in any observed
difference between the control and high CO2 treatments.
Results
Biomass production, elemental composition,
dissolved constituents and nutrient uptake
A considerable increase in phytoplankton biomass (both
Chl a and POC) was observed in all treatments (Figure
2 b–d) with a concomitant decrease in all dissolved inorganic nutrients (Table 1) and POP (Figure 2 d). DIC concentration decreased <4.5% on average for all treatments
(Table 2). However, biomass build-up did not show any
significant CO2 -induced effect. The ratios of Chl a to
CURRENT SCIENCE, VOL. 108, NO. 10, 25 MAY 2015
POC (Figure 2 e) increased 5.65 times and showed a
decreasing trend with increasing CO2 levels; however, it
was statistically insignificant (P > 0.1). The initial ratios
of POC : PON (Figure 2 f ), POC : POP ratio (Figure 2 g)
and PON : POP (Figure 2 h) increased considerably in the
final samples.
DOC concentrations (initial value was 127.67 
2.42 M) showed very small change over the experiment
(125.65  2.6 M; Table 1) and no clear CO2 effect was
observed. Net uptake of DIC from each CO2 treatment
(Table 2) revealed that DIC uptake was 4.83%, 3.73%
and 3.22% of the initial DIC values in control, 640 and
730 atm pCO2 treatments respectively. Interestingly, the
net build-up of POC + DOC (final values of POC +
DOC were deducted from the initial values) revealed a
poor correlation with net DIC uptake in the high CO2
treatments. In control, net DIC uptake and POC + DOC
build-up values was very close with only 2.13% deviation, whereas, for two high CO2 treatments these values
deviated by 27.12% and 22.12% respectively (Table 2).
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Table 1. Initial and final day concentrations of dissolved inorganic nutrients (DIN, DIP and silicate), DOC, POC, PON,
POP) in different pCO 2 treatments (DIN = NO3 + NO2 + NH4 ). Si : N is the consumption ratio calculated by dividing the
net Si uptake by net DIN uptake. Values given are average ( SD) and n = 3. The reported pCO2 values are of the initial day
Initial
Table 2.
Dissolved
fraction
pCO 2 (atm)
DIN (M)
Silicate (M)
DIP (M)
DIN : DIP
N : Si
Si : N
DOC (M)
17.2  2.07
5.92  0.41
0.52  0.13
33.7  5.5
2.9  0.15
–
125  2.6
 460 (control)
4.46  1.4
2.23  0.07
0.02  0.01
318  222
2.01  0.67
0.75  0.12
127  4
 640
6.45  1.65
2.04  0.17
0.01  0.01
542  142
3.71  0.49
1.03  0.17
142  26
 730
5.71  0.46
2.14  0.23
0.02  0.01
342  236
2.68  0.2
0.83  0.02
130  1.7
Particulate
fraction
POC (M)
PON (M)
POP (M)
POC : PON
POC : POP
PON : POP
59.1  14
7.64  1.9
0.33  0.08
7.7  0.1
178.3  0.4
23  0.1
130  21
10.5  1.0
0.19  0.03
12.5  2.5
691  65
56.7  9.6
138  10.18
10.5  0.5
0.21  0.05
13.2  1.2
672  213
50.4  12.1
116  12.71
8.9  0.41
0.15  0.03
13.2  1.5
820  210
62.5  10.8
Net DIC uptake, net growth of POC + DOC, percentage change in DIC and percentage deviation between POC + DOC build-up and DIC
uptake for different CO 2 levels during the experimental period
Initial pCO2 levels
(atm)
 460 (control)
 640
 730
Net change in DIC
(mol l–1 )
99.87  3.8
83.88  7.9
70.77  2.91
Net production of POC + DOC
over the experimental
period (mol l–1 )
102  18.02
115  17.66
91  12
The amount of net nutrient uptake was calculated by
subtracting the final concentrations from their initial values
and has been presented in Figure 3 a–c. The net DIN uptake in the untreated controls was almost 21% higher
relative to the high CO2 -treated cells (Figure 3 a; t = 2.17,
df = 7, P < 0.10). Conversely, net uptake of Si and DIP
(Figure 3 b and c) did not show any significant difference
within different CO2 levels. However, the Si : N and N : P
uptake rates were significantly higher at the elevated CO2
levels (Figure 3 d and f ; df = 7, P < 0.05). The Si : P
uptake rates (Figure 3 e) did not show any significant difference between the treatments.
 13C and  15N of particulate organic matter
 13CPOM in the untreated controls was changed (–22.7 
0.2‰) by 0.3‰ from its initial value (–22.3  0.08‰)
(Figure 4 a). A significant negative linear correlation
(P < 0.001) was observed between the  13CPOM values
and CO2 levels (Figure 4 b; t = 5.37, df = 7, P < 0.002).
The average value of  15 NPOM (Figure 4 c) on the final
day (3.1‰) was almost 15 times higher than that on the
initial day (0.2‰), indicating high rate of nitrate uptake,
which is consistent with the depleted values of DIN
on the final day. The average value of  15 NPOM in the
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Final
Percentage
change in DIC
4.83  0.19
3.73  0.41
3.22  0.13
Percentage
deviation between POC + DOC
and DIC uptake
2.13
27.12
22.12
controls was 1.2 times higher than that of high CO 2 incubated cells, indicating relatively higher nitrate uptake in
the controls which was consistent with the net DIN
uptake.
HPLC pigment analysis
Phytoplankton community structure was examined by
HPLC and flow cytometry. Fucoxanthin (Fuco), the
marker pigment of diatoms was the major contributor
among all other pigments. Initial Fuco/Chl a ratio was
1 : 1 and remained unchanged in the untreated controls.
Fuco/Chl a increased by 1.29 times in the high CO2
treatments (Figure 5 a; significant at 95% confidence
level; t = 2.20, df = 7, P < 0.05). Diadinoxanthin (DD)
and diatoxanthin (DT) were also detected from the cell
extracts. DT index [DT/(DT + DD)] was high in the
untreated controls and decreased in the high CO 2 treated
cells (Figure 5 b; t = 2.44, df = 7, P < 0.05).
Most of the pigments usually present in the members
of chlorophyta, namely lutein (Lut), chlorophyll b
(Chl b), violoxanthin (Viol), neoxanthin (Neo), antheraxanthin (Anth) and zeaxanthin (Zea) were also detected
from HPLC analysis, but their concentrations were much
lower relative to Fuco concentration. However, the quantity
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Figure 3. Net uptake of (a) DIN, (b) Si, (c) DIP and the ratios of net uptake of (d) Si : N, (e) Si:P and ( f ) N : P
under different CO 2 levels over the experimental range (average  SD, n = 3).
and distribution of these pigments showed considerable
variation in relation to increasing CO2 levels. All representative pigments of chlorophyta showed a clear decreasing trend with increasing CO2 levels. The values of
Lut/Chl a (Figure 5 c), Chl b/Chl a (Figure 5 d) and
Neo/Chl a (Figure 5 e) significantly reduced under elevated CO2 levels than those of the control (P < 0.05). Lut,
the marker pigment of the group chlorophyta and
Lut/Chl a ratio revealed a significant linear negative correlation (P < 0.01) with increasing CO2 levels (Figure
5 c), indicating decreased abundance of the members of
Chlorophyta under the elevated CO2 levels. On the contrary, Viol/Chl a ratio showed a significant negative
linear correlation (P < 0.01) with increasing CO2 levels
(Figure 5 f; P < 0.05). The ratios of Viola/Anth (Figure
5 g) and Viola/Zea (Figure 5 h) showed a similar trend
and considerably reduced with increasing CO2 levels
(P < 0.05). Viola/Zea revealed a significant negative
correlation (P < 0.01) with increasing CO2 levels. Alloxanthin (Allo) the marker pigment of cryptophytes and
Allo/Chl a ratios showed statistically insignificant
(t = 0.95, df = 7, P > 0.1) relation with the CO2 levels.
Prasinophytes are usually denoted by the pigment prasinoxanthin (Pras) and are a subgroup under the division
chlorophyta. They are usually detected in flow cytometry
as pico-eukaryotes. In the present experiment Pras/Chl a
ratios decreased significantly (t = 2.54, df = 7, P < 0.05)
in the high CO2 treated cells, indicating their reduced
abundance (figure not shown here).
CURRENT SCIENCE, VOL. 108, NO. 10, 25 MAY 2015
Flow cytometry analysis
Flow cytometry analysis revealed the presence of Synechococcus, heterotrophic bacteria and pico-eukaryotes in
the experimental samples. Prochlorococcus was not
detected. Synechococcus cell abundance (varying between 2.19  104 and 4.39  104 cells ml–1 ) decreased in
the high CO2 treatments compared to the controls (Figure
6 a; t = 3.53, n = 27, P < 0.002). The abundance of picoeukaryotes (varying from 1.85  104 to 3.3  104 cells
ml–1) was significantly lower in the controls relative to
the high CO2 treatments (Figure 6 b; t = 3.53, df = 25,
P < 0.001). Flow cytometry analysis revealed that heterotrophic bacterial abundance (varying from 3.45  105 to
5.36  105 cells ml–1) increased linearly in response to
increasing CO2 levels (Figure 6 c) and showed a significant linear positive correlation (P > 0.01).
Discussions
 13CPOM,  15NPOM and evidence of carbon
concentration mechanism operation
In the untreated controls, net increase in POC and C : N
ratios indicated that a substantial amount of DIC was
converted to POM. From the control treatment, slightly
depleted values of  13CPOM in the post-incubation samples
indicate the possibility of bicarbonate ions uptake as a
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major source of DIC. Since, bicarbonate uptake contributes to low discrimination of 13C ( 13C  0‰) compared
to the diffusive CO2 uptake ( 13 C  –8‰)21 it is indicated
by unaltered or slightly modified values of  13 CPOM after
incubation under the low CO2 levels22. Dissolved CO2
concentration in seawater is <1% of total DIC and the
half-saturation constant (CO2) of Rubisco (carbon-fixing
enzyme) is significantly higher than that23, and thus can
limit the rate of carbon fixation in marine phytoplankton24. In order to fix carbon efficiently under low CO2
levels, bicarbonate ion is consumed by most of the marine
microalgae (the predominant from of DIC in the marine
waters) through an active carbon concentration mechanism (CCM) at the coast of energy and which is then
converted to CO2 with the help of the enzyme carbonic
anhydrase25 and has been well documented in marine
phytoplankton26–28.
Moreover, CCM requires substantial energy to maintain steady photosynthetic rate under low CO2 levels and
thus, a bigger part of cellular energy is being spent to
build bicarbonate transporter proteins, enzymes, etc. involved in CCM operation. Hence, enhanced CCM activity
may affect nitrogen utilization efficiency and nitrogen
demand can be higher in order to build-up Rubisco and
CCM components29. Net DIN uptake and  15NPOM values
collectively suggest that nitrogen utilization was comparatively higher in the controls than that of high CO2 levels
and this could be related to the building of CCM components. Thus, it is likely that the phytoplankton community
was able to produce significant amount of organic
biomass using bicarbonate ions under the control treatments.
 13CPOM values of high CO2 incubated cells showed
depleted values relative to the initial samples and could
be due to higher diffusive influx of CO2 when external
CO2 concentration increases. A reverse relation between
dissolved CO2 and  13CPOM has been reported from in situ
observations30,31. It has been presumed that upon the increase of external CO2 concentration, marine phytoplankton may benefit by increased diffusive influx of CO2 as
well as by down regulating CCM and hence can grow
faster32. Nonetheless, biomass production was not enhanced in response to higher CO 2 uptake and a similar result has also been observed in other studies33,34. There are
field experiments suggesting that some phytoplankton
groups may have a constitutive CCM26 and thus increased
dissolved CO2 levels may not benefit photosynthesis and
biomass production for those groups.
Dissolved organic carbon leaching in relation to
increased heterotrophic bacterial growth and
dissolved organic carbon
Figure 4. a, Initial and final day values of  13 C of POM from different CO 2 treatments during the experimental period. b, Correlation between final day values of  13 C of POM and initial CO2 levels during
the experimental period. c, Initial and final day values of  15 N of POM
from different CO 2 treatments during the experimental period (average  SD, n = 3).
1906
For the present experiment unaffected biomass build-up
in relation to enhanced CO2 levels could also be due to
the alternation in DOC/POC fractionation. Phytoplankton
can also increase organic carbon production in response
to increasing CO2 levels and before converting it to macromolecule or POM, it can be leached out from the cell as
DOC and may not be reflected in the biomass. It has been
observed that under the increased CO2 levels, DOC production could be higher relative to POC production35,36.
However, as has been mentioned earlier, the DOC concentration remains almost unaffected and that could be
due to simultaneous consumption of DOC by heterotrophic bacteria. Higher heterotrophic bacterial abundance
with increasing CO2 levels indicates the possibility of
higher labile fraction of organic carbon release. It is
likely that the rate of organic carbon production was
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Figure 5. The ratios (g : g) of (a) Fuco/Chl a (b) DT/(DT + DD), (c) Lut/Chl a, (d) Chl b/Chl a, (e) Neo/Chl a,
( f ) Viola/Chl a, (g) Viola/Anth and (h) Viol/Zea under different CO 2 levels over the experimental range
(average  SD, n = 3).
probably enhanced under the elevated CO2 levels and
perhaps a significant amount was leached out and simultaneously consumed by heterotrophic bacterial community, and was also observed in an earlier study8 .
The values of net POC + DOC build-up and DIC
uptake were close in the case of control with only 2.13%
deviation. Interestingly, the same parameters revealed
significant deviation with DIC uptake values (Table 2;
almost >22% deviation) in the high CO 2 treatments. It is
likely that at the elevated CO2 levels due to the availability of more labile organic carbon, heterotrophic respiration increased, which might have contributed some
amount of CO2 to the DIC pool. Hence, DIC values were
higher than expected and shadowed the original uptake
rate. Moreover, if heterotrophic bacteria would respire on
the particulate fraction of organic carbon,  13CPOM values
would have been enriched instead of being depleted.
Considering all these facts together, it is likely that
organic carbon production probably increased under the
high CO2 levels. However, it was not converted to POM
and was perhaps leached out as DOC, which was further
consumed by heterotrophic bacteria.
CURRENT SCIENCE, VOL. 108, NO. 10, 25 MAY 2015
Phytoplankton community and light stress
Increased Fuco/Chl a ratios under the elevated CO2 levels
may not be due to higher abundance of diatoms. Increased
proliferation of diatoms would show higher Si uptake,
and net silicate uptake and Si : P uptake ratio did
not show any concomitant enhancement in response to
increasing CO2 levels. Presumably, it could be due to
enhanced light harvesting pigment (Fuco) synthesis under
high CO2 levels and has been observed in other studies as
well (Biswas et al., unpublished data). HPLC pigment
analysis revealed that the phytoplankton community
expressed the signal of light stress. Phytoplankton groups
in the coastal and marine environment, including diatoms,
dinophytes and haptophytes possess a xanthophyll cycle
under light stress, where they convert DD to DT (by a
single de-epoxidation step)37 and thus dissipate the extra
energy as heat (non-photochemical quenching; NPQ).
Higher DT indexes in the untreated controls relative to
the high CO2 treated cells suggest that the need of NPQ
was higher under low CO2 treatment. Utilization of light
energy depends on the availability of sufficient DIC at
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the site of carboxylation when other resources are optimum. Hence, under saturated light and low CO2 levels
the absorbed light energy could be in excess relative to its
utilization in photosynthesis resulting in net accumulation
of unutilized, energy, which may cause photo-damage.
Under such conditions, higher amount DD should be
converted to DT to dissipate the extra energy resulting in
high DT index. On the other hand, increased external CO2
concentration followed by increasing CO2 influx inside
the cell (as indicated by the  13 CPOM data) enhance the
substrate availability for Rubisco enabling the cells to
utilize greater portion of the absorbed light energy and
hence low DT index. Similarly, in other studies, marine
diatoms have been shown to better counteract light stress
under higher CO2 levels38,39, mostly due to the availability
of more substrate from the Kelvin cycle.
For green algae, a xanthophyll cycle is mainly driven
by Viol, where the conversions of the epoxide Viol into
de-epoxy containing form Zea through Anth takes
place40. Decreased ratios of Viol/Chl a, Viol/Anth and
Viol/Zea with respect to the increasing CO2 levels indicate that the magnitudes of light stress in the members of
Chlorophyta were higher under the elevated CO2 levels
Figure 6. Final day flow-cytometric cell counts of (a)
Synechococcus, (b) pico-eukaryotes and (c) heterotrophic bateria under
different CO 2 levels over the experiment range (average  SD, n =
6–9).
1908
relative to the control. Higher levels of light stress under
low pH have been noticed in green algae41. Presumably,
the sensitivity of diatoms from the study area can be less
to increasing CO2 levels compared to the members of
chlorophyta, pico-eukaryotes and prasinophytes. This
can be directly linked to the sensitivity of their CCM
components and carbon metabolism pathways. In a recent
CO2 perturbation experiment in the Arctic waters9, the
abundance of prasinophytes showed a positive correlation
with the CO2 levels. However, this trend also varied over
different phases of the same experiment. Similar trend
was also observed in other large-scale CO2 enrichment
experiments11. This difference could be explained by the
existence of different modes of CCM operation in these
groups of microalgae, which can even vary in the species
level.
Conclusion
The present study shows some basic facts about the responses of the phytoplankton community to short-term
CO2 enhancement from the Yellow Sea coastal waters.
The results reveal the differential responses of diverse
microalgal groups to increased CO2 levels. Presumably,
the diatom-dominated phytoplankton community can utilize
bicarbonate ion under low CO2 levels. However, higher
diffusive influx of CO2 may not result in higher biomass
production in this phytoplankton community, and might
impact POC/DOC partitioning in marine phytoplankton in
this area. This may further accelerate heterotrophic bacterial activity and hence the carbon dynamics. Further
investigations on a longer timescale would provide better
insight about the same.
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ACKNOWLEDGEMENTS. NIO Contribution number is 5682. This
study was jointly funded by the collaborative project of National Science Foundation (NSF Contract No. 40910212) China and CSIR, India.
We thank the Director, National Institute of Oceanography (NIO), Goa
and Scientist-in-Charge, Regional Centre, NIO, Visakhapatnam for
support and approval for this opportunity to work with the Chinese Scientists. We also thank Drs Dileep Kumar and P. C. Rao (NIO, Goa) for
conducting this India–China collaborative programme. Prof. Guling
Zhang and Jing Ling Ren (Ocean University of China, Qingdao) for
their active co-operation and kind support for conducting this work and
Zhao Min, Yu-Chuan Zhao and Guo-Dong Song (Ocean University of
China, Qingdao) are acknowledged for their kind help.
Received 14 July 2014; revised accepted 28 November 2014
1909